Quantitative MRI reveals differences in striatal myelin in children with DLD

  1. Saloni Krishnan
  2. Gabriel J Cler
  3. Harriet J Smith
  4. Hanna E Willis
  5. Salomi S Asaridou
  6. Máiréad P Healy
  7. Daniel Papp
  8. Kate E Watkins

  • Curated by eLife

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    Evaluation Summary:

    This is a paper that will be of broad interest to cognitive scientists, cognitive neuroscientists and speech pathologists who study language disorders. They apply a new structural neuro-imaging technique - multi-parameter mapping (MPM) to a very large sample of children with and without developmental language disorders. MPM allows them to identify localized structural differences (particularly myelin) that cannot be observed with other techniques. It offers convincing evidence that differences in a range of neural structures--including theoretically motivated left hemisphere language areas, and procedural learning--can be linked to variation in language ability.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)

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Abstract

Developmental language disorder (DLD) is a common neurodevelopmental disorder characterised by receptive or expressive language difficulties or both. While theoretical frameworks and empirical studies support the idea that there may be neural correlates of DLD in frontostriatal loops, findings are inconsistent across studies. Here, we use a novel semiquantitative imaging protocol – multi-parameter mapping (MPM) – to investigate microstructural neural differences in children with DLD. The MPM protocol allows us to reproducibly map specific indices of tissue microstructure. In 56 typically developing children and 33 children with DLD, we derived maps of (1) longitudinal relaxation rate R1 (1/T1), (2) transverse relaxation rate R2* (1/T2*), and (3) Magnetization Transfer saturation (MTsat). R1 and MTsat predominantly index myelin, while R2* is sensitive to iron content. Children with DLD showed reductions in MTsat values in the caudate nucleus bilaterally, as well as in the left ventral sensorimotor cortex and Heschl’s gyrus. They also had globally lower R1 values. No group differences were noted in R2* maps. Differences in MTsat and R1 were coincident in the caudate nucleus bilaterally. These findings support our hypothesis of corticostriatal abnormalities in DLD and indicate abnormal levels of myelin in the dorsal striatum in children with DLD.

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  1. Version published to 10.7554/elife.74242 on eLife
  2. Version published to 10.1101/2021.10.18.464793v2 on bioRxiv
  3. eLife

    Author Response

    Reviewer #1 (Public Review):

    1. In terms of the prior hypothesis here I think the authors justify a prior with respect to striatum and I think the most principled analysis of their hypothesis would be based on volumes of interest in striatum. Figure 1 does show difference in MTsat in striatum between neurotypicals and DLDs but the changes are all in the caudate I think- I cannot see anything in putamen. The authors actually describe changes in only one part of anterior caudate. The authors do describe a number of previous conflicting studies that examine caudate structural changes but that is not their hypothesis. The discussion goes into developmental changes affecting striatum at different times that might be relevant and would require a longitudinal study for a definitive study - as the authors acknowledge.

    The reviewer is correct that at this statistical threshold we only observe MTsat differences in the caudate nucleus. Changes in the putamen did not survive this threshold. Lowering the threshold for MTsat (our maps are openly available on Neurovault), or an ROI analysis (see (https://osf.io/2ba57/)) does not reveal significant statistical differences in the putamen. As we noted in the paper, there are differences in the putamen in R1 (these are also observed in the ROI analysis).

    1. There is a lot of overlap between the caudate signal in the two groups - although the correlation of individual differences is reasonable. The caudate signal would not allow group classification.

    Yes, it is clear that these differences would not be sufficient to allow for group classification of DLD. We have discussed this overlap in the discussion.

    1. Outside of the caudate they do show changes in left IFG and auditory cortex that are hypothesised. But there is a lot else going on - I was struck by occipital changes in figure 1 which are only mentioned once in the manuscript.

    We now discuss these differences in the discussion. Note that we did not have any a priori hypotheses about these regions; to our knowledge, they have not been previously described and are not predicted by any theoretical accounts of DLD.

    1. Should I be concerned by i) apparent signal changes in right anterior lateral ventricle from group comparison in figure 1 ii) signal change correlation in right anterior lateral ventricle in figure 4 (slice 22) and iii) signal change outside the pial surface of the occipital lobe in figure 1?

    No – these may be accounted for by smoothing during analyses. Note, these changes at tissue boundaries are fairly commonly seen in statistical maps following smoothing but are not evident when data are projected onto a 3D surface.

    Reviewer #2 (Public Review):

    This work demonstrates the value that multiparameter mapping imaging protocols can have in uncovering microstructural neural differences in populations with atypical development. Previous studies looking at differences in brain structure have typically used voxel based morphometry (VBM) approaches where differences in volumes can be hard to interpret due to complex tissue compositions. The imaging protocol outlined in this paper can specifically index different tissue properties e.g. myelin, giving a much more sensitive and interpretable measure of structural brain differences. This paper applies this methodology to a population of adolescents with developmental language disorder (DLD). Previous evidence of structural brain differences in DLD is very inconsistent and, indeed, using traditional VBM the authors do not find a difference between children with DLD and those with typical language development. However, they provide convincing evidence that despite no macrostructural differences, children with DLD show clear differences in levels of myelin in the dorsal striatum and in brain regions in the wider speech and language network. This can help to reconcile previous inconsistent findings and provide a useful springboard for both theoretical and empirical work uncovering the nature of the brain bases of language disorders.

    We are grateful for these comments, and to the reviewer for pointing out some key strengths of this work.

    Strengths:

    The imaging protocol is robust and is explained very clearly by the authors. It has been used before in other populations so is an established method but has not been applied to populations of children with DLD before, yielding novel and very interesting results. The authors demonstrate that this is a methodology which could have great value in other populations that display atypical development, increasing the impact of these findings.

    The sample size is large for research in this area which increases confidence in the results and the conclusions.

    Rather than relying solely on group differences in brain microstructure to draw conclusions about neural bases of language development, the authors correlated brain microstructural measures with performance on standardised language tests, allowing stronger inferences to be drawn about the relationships between structure and function. This is often an important omission from developmental neuroimaging work. It gave increased confidence in the finding that alterations in striatal myelin are linked to language difficulties.

    Weaknesses:

    The authors rightly use the CATALISE definition of developmental language disorder, which differs from much of the previous literature by not requiring that children with language difficulties have nonverbal ability that is in the normal range. As can be common when using this definition of DLD, the group with DLD have significantly weaker nonverbal ability than the typically developing group. The authors show that brain microstructural differences correlate with language ability but they don't rule out a correlation with nonverbal or wider cognitive skills. Given the widespread differences in myelination across areas of the brain, including those that weren't predicted e.g. medial temporal lobe, it is plausible that perhaps some of the brain microstructural differences are not linked directly to language impairment but a broader constellation of difficulties. Some of the arguments in the paper would be strengthened if this interpretation could be ruled out.

    To rule out the effect of nonverbal IQ or wider cognitive differences, we have conducted stepwise regression analyses on the quantitative data extracted from the statistical cluster covering the caudate nuclei, assessing the influence of factors such as language proficiency, verbal memory and IQ. We find that language status accounts for the most variance, rather than nonverbal IQ or verbal memory (details are included in the paper).

    We also discuss this point in the discussion, pointing to the presence of co-occurring differences in DLD and how these might account for some of the broader group differences we observe.

    The authors acknowledge in the limitations section that their data cannot speak to whether brain differences are a cause or consequence of language impairment. However, there are some implied assumptions throughout the discussion of the results that brain differences in myelination have functional consequences for language learning. A correlation between structure and function does not indicate this level of causality, particularly in an adolescent population - function could just as easily have had structural consequences or environmental differences could have influenced both structure and function. In my view, the speculations about functional consequences of myelin differences are not fully supported by the data collected.

    The reviewer is correct in saying that the myelin deficit could be either a cause or a consequence of DLD or even that both are caused by a third factor. We specifically address this in the discussion section, and note a longitudinal analysis would be the best way to address this question. Indeed, R3 notes about our paper, “…it does a very good job of avoiding the common trope of assuming neural differences play a causal role in DLD (when in fact, reduced atypical development could cause neural differences)”.

    The data suggest that there is much greater variability in left caudate nucleus MTsat values for the DLD group than the other two groups. The impact this may have on the results is not discussed in the interpretation and it is unclear whether this greater variability occurs throughout all of the key MPM measures for the DLD group.

    Thank you for raising this important issue. In figure 1, we only plot the MTsat values from the caudate nucleus for visualisation, and as you note, there we is a considerable degree of variability within the DLD group. However, and crucially, this difference would not influence statistical interpretation of our results. The whole-brain analysis used involves permutation testing, and is robust to a difference in group variability. However, the issue of variability within DLD is important and we now highlight this in our discussion, noting that not every child with DLD will have reduced striatal myelin. Indeed, this variability is even more evident in figure 4. An important challenge for future studies is to understand the link between striatal myelination and the spectrum of language variability.

    Reviewer #3 (Public Review):

    Developmental Language Disorder (DLD) is observed in children who struggle to learn and use oral language despite no obvious cause. It is extremely wide-spread affecting 7-10% of children, and extremely consequential as it persists throughout life and has downstream effects on reading, academic outcomes, and career success. A large number of prior studies have attempted to identify the structural neural differences that are associated with DLD. These have generally shown mixed results, but support a number of candidate regions including left hemisphere language areas (particularly the inferior frontal gyrus), and striatal regions that are possibly linked to learning. However, these studies have suffered from small sample sizes and conflicting results. Part of this may be their reliance on traditional voxel-based-morphometric techniques which estimate cortical thickness and gray matter density. The authors argue that these measures are biologically imprecise; gray matter can be thinner for example, due to synaptic pruning or increased mylenation.

    The authors of this study offer a powerful new tool for understanding these differences. Multi-Parameter Mapping (MPM) is based on standard MRI techniques but offers several measures with much greater biological precision that can be tied specifically to myelination, a key marker of efficient neural transmission. The test a very large number of children (>150) with and without DLD using MPM and show strong evidence for fundamental biological differences in these children.

    This study features a number of key strengths. First, at the level of neuro-imaging, the MPM technique is new in this population and offers fundamental insight that cannot be obtained by other measures. Indeed, the authors wisely use a traditional gray matter approach (voxel based morphometry) and find few if any differences between children with DLD and typical development. This offers a powerful proof of the sensitivity of this approach. Moreover, the authors analyze their data comprehensively, looking at two measures of myelin (MTsat and R1) and their convergence.

    However, at the most important level, I think structural approaches (like MPM, diffusion weighted imaging and so forth) offer tremendous promise for dealing with this as they avoid the ambiguity associated with interpreting functional MRI. Are children showing reduced BOLD because they are less good at language processing? Or do the differences in brain function cause poorer language processing? Structural approaches - and MPM in particular - offer tremendous promise as they unambiguously assess the fundamental neuro-biology.

    Beyond the neuro-imaging this study is also strong in their sample and the measurements of language. The sample size is very large and an order of magnitude larger than existing studies. It is well characterized, and the authors use a large set of well-motivated measures that capture the relevant dimensionality of language. Moreover, the authors treat language both as a clinical category and a continuous measure which is consistent with current thinking on the nature of DLD as potentially the low end of a continuous scale rather than a discrete disorder.

    Finally, the discussion of this paper for the most part does a good job of fitting these neurobiological findings into our broader understanding of DLD. It does an excellent job of mapping the observed brain differences onto functional differences in the child. Importantly, in doing this it does a very good job of avoiding the common trope of assuming neural differences play a causal role in DLD (when in fact, reduced atypical development could cause neural differences).

    We are very grateful for the reviewer for taking the time to read our work so closely and pointing out these strengths in the work.

    Despite these strengths, I have a number of substantive concerns that if addressed will improve the overall impact of this paper.

    First, as the authors are aware, there is a long running and active debate in DLD as to whether DLD is the tail end of continuous distribution of children or a unique disorder (Leonard, 1987, 1991; Tomblin, 2011; Tomblin & Zhang, 1999). The results here offer great promise for informing that debate. And in that vein the authors quite appropriately analyze their data in two ways: once using DLD as a categorical variable and once using continuous measures of language. However, they don't really attempt to wrestle with the differences between the model.

    We have now included a section on the implications of our results for DLD in the discussion.

    Second, I was a little surprised to see the authors highlight left IFG in the discussion to the degree they did. While there was clear evidence for reduced myelin there in the MTsat analysis, this did not hold up in R1 analysis, and even in the MTsat, IFG was clearly not the primary locus. Rather the areas of differences seemed to be centered at Pre- and Post-Central gyrus and extending ventrally (to IFG) and posteriorly from there. Given debate on the role of IFG in language specific processing in general (Diachek, Blank, Siegelman, Affourtit, & Fedorenko, 2020; Fedorenko, Duncan, & Kanwisher, 2013), it was not immediately clear to me why that area was important to highlight. For example, some of the posterior temporal areas (and motor areas) that were found were equally important for perceptual, lexical and phonological processing that are important for other theories of DLD.

    We do see group differences in left IFG in the R1 analysis (see Figure 2) and they were more extensive than those seen in the MTsat analysis with which they overlapped. The reviewer is correct that the differences were limited to the opercular part of the IFG in both analyses whereas they extended more dorsally in the R1 analysis. They also extended ventrally to the anterior insular cortex. We respectfully disagree with the reviewer about the importance of highlighting these differences, given the importance of this region for language processing, and our previous hypotheses about this region. Even so, we agree that the posterior temporal and motor areas are of equal importance and have highlighted these in the discussion.

    The authors rightly point to their differences in the striatum as supporting theories of DLD centered around differences learning. However, as they discuss, there are also large differences throughout the brain in both perceptual, motor and language areas. These would seem to support theories of DLD centered around processing and representation. In particular, the differences in myelination likely are linked to differences in the efficiency of neural coding. This would seem to favor two theoretical views that might be worth mentioning - speed of processing (Miller, Kail, Leonard, & Tomblin, 2001), and approaches based on lexical processing (McMurray, Klein-Packard, & Tomblin, 2019; McMurray, Samelson, Lee, & Tomblin, 2010; Nation, 2014). I was surprised these were not mentioned, given the clear link to the timecourse of processing. Does then suggest that these theories might complement each other? It would be useful to see some more discussion of the implications of these findings for broader theories.

    We have now incorporated mention of these theories in the discussion and discuss implications. We agree with the reviewer that it would be interesting to see whether the different theories could be reconciled.

  4. eLife

    Evaluation Summary:

    This is a paper that will be of broad interest to cognitive scientists, cognitive neuroscientists and speech pathologists who study language disorders. They apply a new structural neuro-imaging technique - multi-parameter mapping (MPM) to a very large sample of children with and without developmental language disorders. MPM allows them to identify localized structural differences (particularly myelin) that cannot be observed with other techniques. It offers convincing evidence that differences in a range of neural structures--including theoretically motivated left hemisphere language areas, and procedural learning--can be linked to variation in language ability.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)

  5. eLife

    Reviewer #1 (Public Review):

    1. In terms of the prior hypothesis here I think the authors justify a prior with respect to striatum and I think the most principled analysis of their hypothesis would be based on volumes of interest in striatum. Figure 1 does show difference in MTsat in striatum between neurotypicals and DLDs but the changes are all in the caudate I think- I cannot see anything in putamen. The authors actually describe changes in only one part of anterior caudate. The authors do describe a number of previous conflicting studies that examine caudate structural changes but that is not their hypothesis. The discussion goes into developmental changes affecting striatum at different times that might be relevant and would require a longitudinal study for a definitive study - as the authors acknowledge.
    2. There is a lot of overlap between the caudate signal in the two groups - although the correlation of individual differences is reasonable. The caudate signal would not allow group classification.
    3. Outside of the caudate they do show changes in left IFG and auditory cortex that are hypothesised. But there is a lot else going on - I was struck by occipital changes in figure 1 which are only mentioned once in the manuscript.
    4. Should I be concerned by i) apparent signal changes in right anterior lateral ventricle from group comparison in figure 1 ii) signal change correlation in right anterior lateral ventricle in figure 4 (slice 22) and iii) signal change outside the pial surface of the occipital lobe in figure 1?

  6. eLife

    Reviewer #2 (Public Review):

    This work demonstrates the value that multiparameter mapping imaging protocols can have in uncovering microstructural neural differences in populations with atypical development. Previous studies looking at differences in brain structure have typically used voxel based morphometry (VBM) approaches where differences in volumes can be hard to interpret due to complex tissue compositions. The imaging protocol outlined in this paper can specifically index different tissue properties e.g. myelin, giving a much more sensitive and interpretable measure of structural brain differences. This paper applies this methodology to a population of adolescents with developmental language disorder (DLD). Previous evidence of structural brain differences in DLD is very inconsistent and, indeed, using traditional VBM the authors do not find a difference between children with DLD and those with typical language development. However, they provide convincing evidence that despite no macrostructural differences, children with DLD show clear differences in levels of myelin in the dorsal striatum and in brain regions in the wider speech and language network. This can help to reconcile previous inconsistent findings and provide a useful springboard for both theoretical and empirical work uncovering the nature of the brain bases of language disorders.

    Strengths:

    The imaging protocol is robust and is explained very clearly by the authors. It has been used before in other populations so is an established method but has not been applied to populations of children with DLD before, yielding novel and very interesting results. The authors demonstrate that this is a methodology which could have great value in other populations that display atypical development, increasing the impact of these findings.

    The sample size is large for research in this area which increases confidence in the results and the conclusions.

    Rather than relying solely on group differences in brain microstructure to draw conclusions about neural bases of language development, the authors correlated brain microstructural measures with performance on standardised language tests, allowing stronger inferences to be drawn about the relationships between structure and function. This is often an important omission from developmental neuroimaging work. It gave increased confidence in the finding that alterations in striatal myelin are linked to language difficulties.

    Weaknesses:

    The authors rightly use the CATALISE definition of developmental language disorder, which differs from much of the previous literature by not requiring that children with language difficulties have nonverbal ability that is in the normal range. As can be common when using this definition of DLD, the group with DLD have significantly weaker nonverbal ability than the typically developing group. The authors show that brain microstructural differences correlate with language ability but they don't rule out a correlation with nonverbal or wider cognitive skills. Given the widespread differences in myelination across areas of the brain, including those that weren't predicted e.g. medial temporal lobe, it is plausible that perhaps some of the brain microstructural differences are not linked directly to language impairment but a broader constellation of difficulties. Some of the arguments in the paper would be strengthened if this interpretation could be ruled out.

    The authors acknowledge in the limitations section that their data cannot speak to whether brain differences are a cause or consequence of language impairment. However, there are some implied assumptions throughout the discussion of the results that brain differences in myelination have functional consequences for language learning. A correlation between structure and function does not indicate this level of causality, particularly in an adolescent population - function could just as easily have had structural consequences or environmental differences could have influenced both structure and function. In my view, the speculations about functional consequences of myelin differences are not fully supported by the data collected.

    The data suggest that there is much greater variability in left caudate nucleus MTsat values for the DLD group than the other two groups. The impact this may have on the results is not discussed in the interpretation and it is unclear whether this greater variability occurs throughout all of the key MPM measures for the DLD group.

  7. eLife

    Reviewer #3 (Public Review):

    Developmental Language Disorder (DLD) is observed in children who struggle to learn and use oral language despite no obvious cause. It is extremely wide-spread affecting 7-10% of children, and extremely consequential as it persists throughout life and has downstream effects on reading, academic outcomes, and career success. A large number of prior studies have attempted to identify the structural neural differences that are associated with DLD. These have generally shown mixed results, but support a number of candidate regions including left hemisphere language areas (particularly the inferior frontal gyrus), and striatal regions that are possibly linked to learning. However, these studies have suffered from small sample sizes and conflicting results. Part of this may be their reliance on traditional voxel-based-morphometric techniques which estimate cortical thickness and gray matter density. The authors argue that these measures are biologically imprecise; gray matter can be thinner for example, due to synaptic pruning or increased mylenation.

    The authors of this study offer a powerful new tool for understanding these differences. Multi-Parameter Mapping (MPM) is based on standard MRI techniques but offers several measures with much greater biological precision that can be tied specifically to myelination, a key marker of efficient neural transmission. The test a very large number of children (>150) with and without DLD using MPM and show strong evidence for fundamental biological differences in these children.

    This study features a number of key strengths. First, at the level of neuro-imaging, the MPM technique is new in this population and offers fundamental insight that cannot be obtained by other measures. Indeed, the authors wisely use a traditional gray matter approach (voxel based morphometry) and find few if any differences between children with DLD and typical development. This offers a powerful proof of the sensitivity of this approach. Moreover, the authors analyze their data comprehensively, looking at two measures of myelin (MTsat and R1) and their convergence.

    However, at the most important level, I think structural approaches (like MPM, diffusion weighted imaging and so forth) offer tremendous promise for dealing with this as they avoid the ambiguity associated with interpreting functional MRI. Are children showing reduced BOLD because they are less good at language processing? Or do the differences in brain function cause poorer language processing? Structural approaches - and MPM in particular - offer tremendous promise as they unambiguously assess the fundamental neuro-biology.

    Beyond the neuro-imaging this study is also strong in their sample and the measurements of language. The sample size is very large and an order of magnitude larger than existing studies. It is well characterized, and the authors use a large set of well-motivated measures that capture the relevant dimensionality of language. Moreover, the authors treat language both as a clinical category and a continuous measure which is consistent with current thinking on the nature of DLD as potentially the low end of a continuous scale rather than a discrete disorder.

    Finally, the discussion of this paper for the most part does a good job of fitting these neurobiological findings into our broader understanding of DLD. It does an excellent job of mapping the observed brain differences onto functional differences in the child. Importantly, in doing this it does a very good job of avoiding the common trope of assuming neural differences play a causal role in DLD (when in fact, reduced atypical development could cause neural differences).

    Despite these strengths, I have a number of substantive concerns that if addressed will improve the overall impact of this paper.

    First, as the authors are aware, trhere is a long running and active debate in DLD as to whether DLD is the tail end of continuous distribution of children or a unique disorder (Leonard, 1987, 1991; Tomblin, 2011; Tomblin & Zhang, 1999). The results here offer great promise for informing that debate. And in that vein the authors quite appropriately analyze their data in two ways: once using DLD as a categorical variable and once using continuous measures of language. However, they don't really attempt to wrestle with the differences between the model.

    Second, I was a little surprised to see the authors highlight left IFG in the discussion to the degree they did. While there was clear evidence for reduced myelin there in the MTsat analysis, this did not hold up in R1 analysis, and even in the MTsat, IFG was clearly not the primary locus. Rather the areas of differences seemed to be centered at Pre- and Post-Central gyrus and extending ventrally (to IFG) and posteriorly from there. Given debate on the role of IFG in language specific processing in general (Diachek, Blank, Siegelman, Affourtit, & Fedorenko, 2020; Fedorenko, Duncan, & Kanwisher, 2013), it was not immediately clear to me why that area was important to highlight. For example, some of the posterior temporal areas (and motor areas) that were found were equally important for perceptual, lexical and phonological processing that are important for other theories of DLD.

    The authors rightly point to their differences in the striatum as supporting theories of DLD centered around differences learning. However, as they discuss, there are also large differences throughout the brain in both perceptual, motor and language areas. These would seem to support theories of DLD centered around processing and representation. In particular, the differences in myelination likely are linked to differences in the efficiency of neural coding. This would seem to favor two theoretical views that might be worth mentioning - speed of processing (Miller, Kail, Leonard, & Tomblin, 2001), and approaches based on lexical processing (McMurray, Klein-Packard, & Tomblin, 2019; McMurray, Samelson, Lee, & Tomblin, 2010; Nation, 2014). I was surprised these were not mentioned, given the clear link to the timecourse of processing. Does then suggest that these theories might complement each other? It would be useful to see some more discussion of the implications of these findings for broader theories.

  8. Version published to 10.1101/2021.10.18.464793v1 on bioRxiv